deep_feature = [0.23, 0.41, ..., 0.57]
The final deep feature representation for the input text is: deep_feature = [0
bert_embedding(varicad) = [0.1, 0.2, ..., 0.768] bert_embedding(-) = [0.05, 0.05, ..., 0.05] bert_embedding(v2) = [0.3, 0.4, ..., 0.9] ... bert_embedding(2022) = [0.8, 0.9, ..., 0.1] deep_feature = [0.23
This is a dense vector representation of the input text, which can be used for downstream tasks such as text classification, clustering, or information retrieval. 0.768] bert_embedding(-) = [0.05
To generate a deep feature for the text, we can use a text embedding technique such as Word2Vec or BERT. Let's assume we're using a pre-trained BERT model to generate embeddings.
Using a pre-trained BERT model, we generate embeddings for each token:
The input text is tokenized into subwords: